During the history of information technology, it has been a struggle to organize and understand ever increasing volumes of data. While many developments have been made in the area of Relational Database Systems and Business Intelligence Systems to help in the management of structured data, the domain of unstructured and heterogeneous data management is still extremely inefficient. Keyword search technology remains the predominant access model. Recent advances in semantic technology have yielded improvements in the accuracy and relevance of information retrieved by such search operations.
However, the paradigm of search remains poorly suited to the needs of a researcher. Researchers issue queries and receive results, but in many cases the results are overwhelming in number, and much time must be invested in sifting through results to find the pieces of specifically important information. FIG. 1 illustrates a user interface for retrieving query results using a conventional keyword search application. Specifically, in FIG. 1, a Google™ search is illustrated. A keyword, namely, “roller bearing,” is entered into a search box 130. When the Search button 132 is selected, a list of results 134 for “roller bearing” is returned.
Knowledge visualization technology has been developed to help the user better understand the scope and content of result sets. Similarly, ontologically oriented systems of access, such as the semantic web, attempt to provide guidance to the researcher through the exposure of a navigational taxonomy that allows the user to select subsets of information based on taxonomical terms. FIG. 2 illustrates a user interface for data visualization, as an example of a knowledge visualization tool. Specifically, in FIG. 2, a Grokker® search is illustrated. A keyword, namely, “roller bearing,” is entered into a search box 230. When the Search button (“Grok”) 232 is selected, search results 234 returned for “roller bearing” are displayed in a “Map View.”
These conventional navigational systems suffer from a deficiency in that taxonomy is removed from a researcher's intent of analysis. This creates a gap of understanding that the researcher must bridge in order to effectively benefit from the presented view of information.
Another key deficiency with conventional search technologies is their transactional nature. Each search is a completely independent event and there is no persistent information that links events in a research session. This independence of actions is inherently inconsistent with the researcher's intent.
Often, researchers use search technologies not for the purpose of issuing a single query, but for the purpose of delving into a subject area through a series of search requests that are related through the researcher's intent. Current search and concept retrieval tools lack the ability to maintain this connection in a meaningful way. As a result, the research worker must specifically record and track the relationships between related search requests and their generated result sets. This process is very tedious, time consuming, and subject to error if the researcher neglects to capture any part of the process.